Ensemble Learning (also known as Ensembling) is an exciting yet challenging field. Ensembling leverages multiple base models to achieve better predictive performance, which is often better than any of the constituent models alone1. It has been proven critical in many practical applications and data science competitions2, e.g., Kaggle.
To promote the learning of ensembling, we create this repository with:
- Books & Academic Papers
- Online Courses and Videos
- Open-source and Commercial Libraries/Toolboxes and Datasets
- Key Conferences & Journals
More items will be added to the repository. Please feel free to suggest other key resources by opening an issue report, submitting a pull request, or dropping me an email @ ([email protected]). Enjoy reading!
- 1. Books & Tutorials
- 2. Courses/Seminars/Videos
- 3. Toolboxes & Datasets
- 4. Papers
- 5. Key Conferences/Workshops/Journals
Ensemble Methods: Foundations and Algorithms by Zhi-Hua Zhou3: Classical text book covering most of the ensemble learning techniques. A must-read for people in the field. [Full Book]
Ensemble Machine Learning: Methods and Applications edited by Oleg Okun4: Responding to a shortage of literature dedicated to the topic, this volume offers comprehensive coverage of state-of-the-art ensemble learning techniques, including various contributions from researchers in leading industrial research labs.
Applications of Supervised and Unsupervised Ensemble Methods edited by Oleg Okun5: This book contains the extended papers presented at the 2nd Workshop on Supervised and Unsupervised Ensemble Methods and their Applications (SUEMA), in conjunction with ECAI’2008.
Data Mining and Knowledge Discovery Handbook Chapter 45 (Ensemble Methods for Classifiers): by Lior Rokach6: This chapter provides an overview of ensemble methods in classification tasks. We present all important types of ensemble method including boosting and bagging. Combining methods and modeling issues such as ensemble diversity and ensemble size are discussed.
Outlier Ensembles: An Introduction by Charu Aggarwal and Saket Sathe7: Great intro book for ensemble learning in outlier analysis.
| Tutorial Title | Venue | Year | Ref | Materials |
|---|---|---|---|---|
| On the Power of Ensemble: Supervised and Unsupervised Methods Reconciled | SDM | 2010 | 8 | [HTML] |
Coursera - How to Win a Data Science Competition: Learn from Top Kagglers:
Coursera - Machine Learning: Classification by University of Washington partly covers the topic:
Machine Learning and Data Mining by Prof. Alexander Ihler: Section on ensembling (4 videos).
3. Toolboxes & Datasets ---------------------
[Python] combo: combo is a comprehensive Python toolbox for combining machine learning (ML) models and scores for various tasks, including classification, clustering, and anomaly detection. It supports the combination of ML models from core libraries such as scikit-learn and xgboost (documentation).
[Python] pycobra: python library implementing ensemble methods for regression, classification and visualisation tools including Voronoi tesselations.
[Python] DESlib: A Python library for dynamic classifier and ensemble selection.
[Python] imbalanced-learn: A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning (documentation).
As a subfield of machine learning, ensemble learning is usually tested against general machine learning benchmark datasets. Some helpful links can be found below:
- List of datasets for machine-learning research - Wikipedia
- UCI Machine Learning Repository
- PMLB: a large benchmark suite for machine learning evaluation and comparison9: Dataset Repository
| Paper Title | Venue | Year | Ref | Materials |
|---|---|---|---|---|
| Ensemble methods in machine learning | MCS | 2000 | 10 | |
| Popular ensemble methods: An empirical study | JAIR | 1999 | 11 | |
| Ensemble learning: A survey | Wiley Interdisciplinary Reviews | 2018 | 12 |
| Abbreviation | Paper Title | Venue | Year | Ref | Materials |
|---|---|---|---|---|---|
| Bagging | Bagging predictors | Machine Learning | 1996 | 13 | [PDF] |
| Boosting | A decision-theoretic generalization of on-line learning and an application to boosting | JCSS | 1997 | 14 | [PDF] |
| N/A | Bagging, Boosting, and C4.5 | AAAI/IAAI | 1996 | 15 | [PDF] |
| Stacking | Stacked generalization | Neural Networks | 1992 | 16 | [PDF] |
| Stacking | Stacked regressions | Machine Learning | 1996 | 17 | [PDF] |
| Paper Title | Venue | Year | Ref | Materials |
|---|---|---|---|---|
| Xgboost: A scalable tree boosting system | KDD | 2016 | 18 | [PDF] |
| Lightgbm: A highly efficient gradient boosting decision tree | NIPS | 2017 | 19 | [PDF] |
| CatBoost: unbiased boosting with categorical features | NIPS | 2018 | 20 | [PDF] |
| Paper Title | Venue | Year | Ref | Materials |
|---|---|---|---|---|
| Cluster Ensembles – A Knowledge Reuse Framework for Combining Multiple Partitions | JMLR | 2002 | 21 | [PDF] |
| Clusterer Ensemble | KBS | 2006 | 22 | [PDF] |
| A survey of clustering ensemble algorithms | IJPRAI | 2011 | 23 | [PDF] |
| Clustering ensemble method | Cybernetics | 2019 | 24 | [PDF] |
| Paper Title | Venue | Year | Ref | Materials |
|---|---|---|---|---|
| Outlier ensembles: position paper | SIGKDD Explorations | 2013 | 25 | [PDF] |
| Ensembles for unsupervised outlier detection: challenges and research questions a position paper | SIGKDD Explorations | 2014 | 26 | [PDF] |
| Isolation forest | ICDM | 2008 | 27 | [PDF] |
| Outlier detection with autoencoder ensembles | SDM | 2017 | 28 | [PDF] |
| An Unsupervised Boosting Strategy for Outlier Detection Ensembles | PAKDD | 2018 | 29 | [HTML] |
| LSCP: Locally selective combination in parallel outlier ensembles | SDM | 2019 | 30 | [PDF] |
| Paper Title | Venue | Year | Ref | Materials |
|---|---|---|---|---|
| A survey on ensemble learning for data stream classification | ACM Computing Surveys | 2017 | 31 | [PDF] |
| Ensemble learning for data stream analysis: A survey | Information Fusion | 2017 | 32 | [PDF] |
Key data mining conference deadlines, historical acceptance rates, and more can be found data-mining-conferences.
ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD)
ACM International Conference on Management of Data (SIGMOD)
IEEE International Conference on Data Mining (ICDM)
SIAM International Conference on Data Mining (SDM)
IEEE International Conference on Data Engineering (ICDE)
ACM InternationalConference on Information and Knowledge Management (CIKM)
ACM International Conference on Web Search and Data Mining (WSDM)
The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)
ACM Transactions on Knowledge Discovery from Data (TKDD)
IEEE Transactions on Knowledge and Data Engineering (TKDE)
ACM SIGKDD Explorations Newsletter
Data Mining and Knowledge Discovery
Knowledge and Information Systems (KAIS)
Opitz, D. and Maclin, R., 1999. Popular ensemble methods: An empirical study. Journal of artificial intelligence research, 11, pp.169-198.↩
Bell, R.M. and Koren, Y., 2007. Lessons from the Netflix prize challenge. SIGKDD Explorations, 9(2), pp.75-79.↩
Zhou, Z.H., 2012. Ensemble methods: foundations and algorithms. Chapman and Hall/CRC.↩
Zhang, C. and Ma, Y. eds., 2012. Ensemble machine learning: methods and applications. Springer Science & Business Media.↩
Okun, O. ed., 2009. Applications of supervised and unsupervised ensemble methods (Vol. 245). Springer.↩
Rokach L. (2005) Ensemble Methods for Classifiers. In: Maimon O., Rokach L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA↩
Aggarwal, C.C. and Sathe, S., 2017. Outlier ensembles: An introduction. Springer.↩
Gao, J., Fan, W. and Han, J., 2010. On the power of ensemble: Supervised and unsupervised methods reconciled. In Tutorial on SIAM Data Mining Conference (SDM), Columbus, OH.↩
Olson, R.S., La Cava, W., Orzechowski, P., Urbanowicz, R.J. and Moore, J.H., 2017. PMLB: a large benchmark suite for machine learning evaluation and comparison. BioData mining, 10(1), p.36.↩
Dietterich, T.G., 2000, June. Ensemble methods in machine learning. In International workshop on multiple classifier systems (pp. 1-15). Springer, Berlin, Heidelberg.↩
Opitz, D. and Maclin, R., 1999. Popular ensemble methods: An empirical study. Journal of artificial intelligence research, 11, pp.169-198.↩
Sagi, O. and Rokach, L., 2018. Ensemble learning: A survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 8(4), p.e1249.↩
Breiman, L., 1996. Bagging predictors. Machine learning, 24(2), pp.123-140.↩
Freund, Y. and Schapire, R.E., 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of computer and system sciences, 55(1), pp.119-139.↩
Quinlan, J.R., 1996, August. Bagging, boosting, and C4.5. In AAAI/IAAI, Vol. 1 (pp. 725-730).↩
Wolpert, D.H., 1992. Stacked generalization. Neural networks, 5(2), pp.241-259.↩
Breiman, L., 1996. Stacked regressions. Machine learning, 24(1), pp.49-64.↩
Chen, T. and Guestrin, C., 2016, August. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794). ACM.↩
Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. and Liu, T.Y., 2017. Lightgbm: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems (pp. 3146-3154).↩
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. and Gulin, A., 2018. CatBoost: unbiased boosting with categorical features. In Advances in Neural Information Processing Systems (pp. 6638-6648).↩
Strehl, A. and Ghosh, J., 2002. Cluster ensembles---a knowledge reuse framework for combining multiple partitions. Journal of machine learning research, 3(Dec), pp.583-617.↩
Zhou, Z.H. and Tang, W., 2006. Clusterer ensemble. Knowledge-Based Systems, 19(1), pp.77-83.↩
Vega-Pons, S. and Ruiz-Shulcloper, J., 2011. A survey of clustering ensemble algorithms. International Journal of Pattern Recognition and Artificial Intelligence, 25(03), pp.337-372.↩
Alqurashi, T. and Wang, W., 2019. Clustering ensemble method. International Journal of Machine Learning and Cybernetics, 10(6), pp.1227-1246.↩
Aggarwal, C.C., 2013. Outlier ensembles: position paper. ACM SIGKDD Explorations Newsletter, 14(2), pp.49-58.↩
Zimek, A., Campello, R.J. and Sander, J., 2014. Ensembles for unsupervised outlier detection: challenges and research questions a position paper. ACM Sigkdd Explorations Newsletter, 15(1), pp.11-22.↩
Liu, F.T., Ting, K.M. and Zhou, Z.H., 2008, December. Isolation forest. In International Conference on Data Mining, pp. 413-422. IEEE.↩
Chen, J., Sathe, S., Aggarwal, C. and Turaga, D., 2017, June. Outlier detection with autoencoder ensembles. SIAM International Conference on Data Mining, pp. 90-98. Society for Industrial and Applied Mathematics.↩
Campos, G.O., Zimek, A. and Meira, W., 2018, June. An Unsupervised Boosting Strategy for Outlier Detection Ensembles. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 564-576). Springer, Cham.↩
Zhao, Y., Nasrullah, Z., Hryniewicki, M.K. and Li, Z., 2019, May. LSCP: Locally selective combination in parallel outlier ensembles. In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM), pp. 585-593. Society for Industrial and Applied Mathematics.↩
Gomes, H.M., Barddal, J.P., Enembreck, F. and Bifet, A., 2017. A survey on ensemble learning for data stream classification. ACM Computing Surveys (CSUR), 50(2), p.23.↩
Krawczyk, B., Minku, L.L., Gama, J., Stefanowski, J. and Woźniak, M., 2017. Ensemble learning for data stream analysis: A survey. Information Fusion, 37, pp.132-156.↩